
We design a robust deep neural network whose performance is not significantly affected by data grid, thus it is invulnerable to noise, out-of-distribution samples and distortions.

The proposed model mitigates the problem of distance saturation in KNN-based models.

The architecture of the proposed network allows the user to go deeper and deeper without the problem of vanishing-gradient. This scheme is capable of analysis of highly complex objects.

We provide thorough empirical and theoretical analysis on the stability and efficiency of the proposed method using the 3D benchmark datasets: ModelNet and ShapeNet [1]}, [2]}.

